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I am going through the Kaggle Digit Recognizer Tutorial and I'm trying to understand how all of this works. I would like to validate a predicted value. Basically, I have a prediction that's wrong, but I want to see what the actual value of that prediction was. I think I am way off:

...

df = pd.read_csv('data/train.csv')

labels = df['label'].values
x_train = df.drop(columns=['label']).values / 255
# trying to produce a crappy dataset for train/test
x_train, x_test, y_train, y_test = train_test_split(x_train, labels, test_size=0.95)


# Purposely trying to get a crappy model so I can learn about validation
model = tf.keras.models.Sequential()
# model.add(tf.keras.layers.Flatten())
# model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu))
model.add(tf.keras.layers.Dense(10, activation=tf.nn.softmax))
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])

model.fit(x_train, y_train, epochs=1)
predictions = model.predict([x_test])


index_to_predict = 0
print('Prediction: ', np.argmax(predictions[index_to_predict]))
print('Actual:     ', predictions.argmax(axis=-1)[index_to_predict])
print(predictions.shape)
vals = x_test[index_to_predict].reshape(28, 28)
plt.imshow(vals)

This yields the following:

enter image description here

How can I get a true 'heres the prediction' and 'heres the actual' breakdown? My logic on getting the actual is definitely off.

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mwilson
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2 Answers2

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The true labels (also sometimes called target values, or ground-truth labels) are stored in y_train and y_test for training and test set respectively. Therefore, you can easily just print that to find the true label:

print('Actual:', y_test[index_to_predict])
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y_test[index_to_predict]

will have the actual label and

predictions[index_to_predict]

should have the predicted probability values for each of your classes.